Covariance density estimation for autoregressive spectral modelling of point processes
type: Item
instance of: scholarly article
title: Covariance density estimation for autoregressive spectral modelling of point processes
zbMATH Open document ID: 0672.62097
DOI: 10.1007/BF00198766
author: P. J. A. Lago
author: A. P. Rocha
author: N. B. Jones
published in: Biological Cybernetics
publication date: 1989-01-01T00:00:00Z
review text: This paper discusses methods for the estimation of the covariance density and conditional intensity function of point processes and presents alternative computational efficient estimation algorithms leading always to positive semidefinite estimates, therefore adequate for autoregressive spectral analysis. Autoregressive spectral modelling of point processes from Yule-Walker type equations and Levinson recursion combined with the minimum AIC or CAT principle is illustrated with neurobiological data.
Mathematics Subject Classification ID: 62M15
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 4100458
zbMATH Keywords: covariance density
zbMATH Keywords: conditional intensity function
zbMATH Keywords: point processes
zbMATH Keywords: estimation algorithms
zbMATH Keywords: positive semidefinite estimates
zbMATH Keywords: autoregressive spectral analysis
zbMATH Keywords: Yule-Walker type equations
zbMATH Keywords: Levinson recursion
zbMATH Keywords: minimum AIC
zbMATH Keywords: CAT principle
zbMATH Keywords: neurobiological data
Wikidata QID: Q52543397
MaRDI profile type: Publication
cites work: A new look at the statistical model identification
cites work: Q5526518
cites work: Remarks on the theory, computation and application of the spectral analysis of series of events
cites work: Some recent advances in time series modeling
full work available at URL: https://doi.org/10.1007/bf00198766
OpenAlex ID: W2094740839
description: scientific article; zbMATH DE number 4100458